Research projects, including those focused on cancer, rely on the manual extraction of information from clinical reports. This process is time-consuming and prone to errors, limiting the efficiency of data-driven approaches in healthcare. To address these challenges, Natural Language Processing (NLP) offers an alternative for automating the extraction of relevant data from electronic health records (EHRs). In this study, we focus on lung and breast cancer due to their high incidence and the significant impact they have on public health. Early detection and effective data management in both types of cancer are crucial for improving patient outcomes. To enhance the accuracy and efficiency of data extraction, we utilized GMV's NLP tool uQuery, which excels at identifying relevant entities in clinical texts and converting them into standardized formats such as SNOMED and OMOP. uQuery not only detects and classifies entities but also associates them with contextual information, including negated entities, temporal aspects, and patient-related details. In this work, we explore the use of NLP techniques, specifically Named Entity Recognition (NER), to automatically identify and extract key clinical information from EHRs related to these two cancers. A dataset from Health Research Institute Hospital La Fe (IIS La Fe), comprising 200 annotated breast cancer and 400 lung cancer reports, was used, with eight clinical entities manually labeled using the Doccano platform. To perform NER, we fine-tuned the bsc-bio-ehr-en3 model, a RoBERTa-based biomedical linguistic model pre-trained in Spanish. Fine-tuning was performed using the Transformers architecture, enabling accurate recognition of clinical entities in these cancer types. Our results demonstrate strong overall performance, particularly in identifying entities like MET and PAT, although challenges remain with less frequent entities like EVOL.

Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT datasets have been introduced recently, they predominantly focus on high-resource languages like English. To better align LLMs across a broad spectrum of languages and tasks, we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual, Multi-turn instruction finetuning dataset, called M2Lingual. It is constructed by first selecting a diverse set of seed examples and then utilizing the proposed Evol taxonomy to convert these seeds into complex and challenging multi-turn instructions. We demonstrate the effectiveness of M2Lingual by training LLMs of varying sizes and showcasing the enhanced performance across a diverse set of languages. We contribute the 2 step Evol taxonomy with the guided generation code: https://github.com/ServiceNow/M2Lingual, as well as the first fully synthetic, general and task-oriented, multi-turn, multilingual dataset built with Evol - M2Lingual: https://huggingface.co/datasets/ServiceNow-AI/ M2Lingual - containing 182K total IFT pairs, covering 70 languages and 17+ NLP tasks.

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purpose Artificial Intelligence Systems (GPAIS) poses model configuration and adaptability challenges at far greater complexity scales than the optimal design of traditional Machine Learning models. Evolutionary Computation (EC) has been a useful tool for both the design and optimization of Machine Learning models, endowing them with the capability to configure and/or adapt themselves to the task under consideration. Therefore, their application to GPAIS is a natural choice. This paper aims to analyze the role of EC in the field of GPAIS, exploring the use of EC for their design or enrichment. We also match GPAIS properties to Machine Learning areas in which EC has had a notable contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we discuss the challenges of harnessing the benefits of EC for GPAIS, presenting different strategies to both design and improve GPAIS with EC, covering tangential areas, identifying research niches, and outlining potential research directions for EC and GPAIS.

Large Language Models (LLMs) have shown impressive adaptability in various fields, yet the optimal pathway of autonomous model evolution remains underexplored. Drawing inspiration from the self-driven learning process of humans, we introduce SELF (Self-Evolution with Language Feedback), a novel learning framework that empowers LLMs to continually self-improve their abilities. SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement. SELF employs language-based feedback for detailed and nuanced evaluations, pinpointing response flaws and suggesting refinements. Subsequently, the model engages in an iterative process of self-evolution: they autonomously generate responses to unlabeled instructions, refine these responses interactively, and use the refined and filtered data for iterative self-training, thereby progressively boosting their capabilities. Moreover, the SELF framework equips the model with the ability to self-refine during inference, leading to further improved response quality. Our experiments on mathematical and general tasks demonstrate that SELF enables the model to continually selfimprove without human intervention. The SELF framework indicates a promising direction for the autonomous evolution of LLMs, transitioning them from passive information receivers to active participants in their development. Large Language Models (LLMs), like ChatGPT (OpenAI, 2022) and GPT-4 (OpenAI, 2023), stand at the forefront of the AI revolution, demonstrating versatility across tasks. Despite their evident capabilities, the way towards achieving autonomous development of LLMs is still under-explored. The development of automatically improved LLMs can draw inspiration from human self-driven learning mechanisms. When facing new challenges, humans naturally engage in a learning cycle of initial attempts, introspective feedback, and behavior refinement. This leads to a critical question: "Can LLMs mimic the human learning process, utilizing self-refinement to enhance their inherent capabilities?"

Artificial intelligence (AI) can be described as an autonomous and self-evolving system that can recognise and learn from unknown and unpredictable data patterns. AI systems can continuously evolve and learn and improve their domain adaptation and self-organization - after being designed. While this creates many opportunities for self-improving evolving systems, it also creates risks from such systems being used by adversaries against its original intentions. There is a growing concern caused by the increased adoption of artificial intelligence (AI) in predictive cybersecurity, triggering various discussions on the'Skynet' becoming a reality. These fears are amplified by public figures (e.g., Stephen Hawkins) and tech gurus (e.g., Elon Musk) arguing that AI is a serious risk to humanity and could result in

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications in parameter tuning, experimental design, robotics, and so on. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing some acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i.e., upper confidence bound (UCB), we prove a general bound on the cumulative regret, and show that the generation of pseudo-points can improve the instantaneous regret. Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clinical translation of such models is limited by the lack of insight 1) on the risk of error for predictions at patient-level, and 2) on the most adequate time to administer the non-invasive ventilation. To address these issues, we combine Conformal Prediction (a machine learning framework that complements predictions with confidence measures) and a mixture experts into a prognostic model which not only predicts whether an ALS patient will suffer from respiratory insufficiency but also the most likely time window of occurrence, at a given reliability level. Promising results were obtained, with near 80% of predictions being correctly identified.